Optimal testing situations for the automated analysis of cognitive components in natural language : a systematic literature review.
Bibliographic record
Abstract
This review focuses on cognitive components that can be inferred from natural language production in the general and the pathological adult population.Specifically, it targets cognitive functions which are reflected in discourse characteristics.The domain includes studies employing automated or semi-automated methods to assess or model cognitive performance based on spoken language.The review aims to identify testing situations (tasks, contexts, and modalities) that best capture these cognitive components through automated analysis. Rationale for the reviewAdvances in (semi)-automated analysis of speech have enabled the identification of linguistic markers associated with cognitive components.However, current studies rely on highly heterogeneous testing situations, including spontaneous narratives, picture descriptions, interviews, or task-based elicitation, which substantially influence linguistic output and the validity of the cognitive indicators derived from it.The lack of methodological standardization limits comparability across studies and hinders the identification of optimal testing conditions for reliable cognitive assessment through automated language analysis.Moreover, understanding how outcomes from (semi)-automated analysis of PROSPERO International prospective register of systematic reviews speech correspond to those obtained through traditional standardized tests is essential to evaluate the degree of convergence between these two assessment modalities.This comparison is critical to assess the ecological validity and potential clinical utility of automated language analysis.A systematic synthesis is therefore needed to (1) map the testing situations currently used in the literature, (2) evaluate their methodological characteristics, and (3) determine which conditions most effectively reveal cognitive components in natural language production while maintaining consistency with standardized cognitive assessments. Review objectives1. What are the situations used for stimulating spontaneous language ? 2. What are the indicators that reflect cognitive functioning in spontaneous language ? 3. Can softwares detect cognitive markers in language analysis ?Can this detection by the software be automated ? 4. Does cognitive data extraction using software correspond to data obtained from a traditional cognitive assessment ?
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".